Virtual reality imaging system

ABSTRACT

The virtual reality imaging system takes a multidimensional space that contains real world objects and phenomena, be they static or dynamic in nature, and enables a user to define a point and/or a path through this multidimensional space. The apparatus then displays the view to the user that would be seen from the point and/or path through the multidimensional space. This view is filtered through user definable characteristics that refine the real world phenomena and objects to a perspective that is of interest to the user. This filtered view presents the user with a virtual view of the reality contained within this multidimensional space, which virtual reality presents data to the user of only objects, views and phenomena that are of particular interest to the user.

FIELD OF THEE INVENTION

This invention relates to computer generated images and, in particular,to a system that creates a visual image of a multidimensional space topresent a filtered image of various three dimensional phenomena andfeatures that are contained within the multidimensional space as viewedfrom any predefined locus within the space.

PROBLEM

It is a problem in complex computer controlled systems that deal withreal world phenomena to present a representation of the phenomena in amanner that is both informative to the user and in a simple presentationformat. Computer generated graphics are ubiquitous and are typicallyused to present an accurate representation of an object, a phenomena, amultidimensional space and interactions therebetween. Computer generatedgraphics are also used extensively in simulation systems to present animage of a real world situation or a hypothetical situation to a userfor training, analysis or other purposes. Computer generated graphicshave become extremely sophisticated and can represent extremely complexand fanciful situations in a manner that is virtually lifelike. Theapplication of computer graphics spans many technologies andapplications.

One area in which computer graphics has yet to make a significant impactis the area of real time display of complex real world phenomena. Someelementary work has taken place in this area but systems of greatflexibility and adaptability that can handle extremely complex phenomenaare presently unavailable. It is therefore a problem to visually displaya complex multidimensional and real time phenomena in a largemultidimensional space in a sample manner that maps the reality to apredefined user's viewpoint.

SOLUTION

The above described problems are solved and a technical advance achievedin the field by the virtual reality image generation system of thepresent invention. This apparatus takes a multidimensional space thatcontains real world objects and phenomena, be they static or dynamic innature, and enables a user to define a point and/or a path through thismultidimensional space. The apparatus then displays the view to the userthat would be seen from the point and/or path through themultidimensional space. This view is filtered through user definablecharacteristics that refine the real world phenomena and objects to aperspective that is of interest to the user. This filtered view presentsthe user with a virtual view of the reality contained within thismultidimensional space, which virtual reality presents data to the userof only objects, views and phenomena that are of particular interest tothe user. This apparatus highlights, emphasizes, deletes, and reorientsthe reality contained within the multidimensional space to present animage to the user of only what the user needs to see to accomplish astated task. The selective presentation of information in real time ofreal world phenomena enables the user to process the reduced data setcontained in the image presented by this apparatus to perform adesignated task in a manner that heretofore was impossible.

The preferred embodiment described herein is that of an airportoperations system wherein an airport is located in a predeterminedlocation in a multidimensional space and is surrounded by various threedimensional topological surface features. The three dimensional airspace surrounding the airport is typically managed by air trafficcontrollers to route aircraft in the vicinity of the airport intoarrival and departure patterns that avoid the topological features,various weather conditions around the airport, and other aircraft thatshare the airspace with a particular flight. This problem is extremelycomplex in nature in that the multidimensional space around the airportcontains fixed objects such as the 10 airport and its surroundingtopological features as well as dynamic phenomena such as meteorologicalevents that are beyond the control of the air traffic controllers aswell as dynamic phenomena, such as the aircraft, that can be indirectlycontrolled by the air traffic controllers. The dynamic phenomena vary intime and space and the movement of the aircraft within thismultidimensional space must be managed in real time in response to realtime and sometimes sudden changes in the meteorological phenomena aswell as the position of other aircraft.

No known system even remotely approaches providing the air trafficcontrollers, the pilots or other potential users with a reasonabledistillation of all of the data contained with the multidimensionalspace around an airport. Existing airport operations include asignificant amount of data acquisition instrumentation to provide theair traffic controllers as well as the pilots of the aircraft with datarelating to weather, air traffic and spatial relationships of theaircraft with respect to the airport and the ground level. The problemwith this apparatus is that all of the data acquisition instrumentationis configured into individual units, each adapted to present one set ofnarrowly defined relevant information to the user with little attempt tointegrate the plurality of systems into a universal instrument that canbe adapted to controllably provide an image of the multidimensionalspace to the various users, with each image being presented to a user interms of their specific need for information. This is especiallyimportant since the air traffic controller has a significantly differentneed for information than the pilot of the aircraft.

The apparatus of the present invention obtains data from a multitude ofdata acquisition sources and controllably melds this information into adatabase that represents all the information of interest relating tothis multidimensional space. Graphic processing apparatus responds touser input to define a predetermined point or path through themultidimensional space as well as certain visualization characteristicsfor each individual user. The graphic processing apparatus thence, inreal time, presents the user with a customized view of themultidimensional space in a visual form by deleting information that isextraneous or confusing and presenting only the data that is ofsignificant relevance to the particular user as defined by the filter.In an airport operation environment, low level wind shear alert systems(LLWAS) use ground-based sensors to generate data indicative of thepresence and locus of meteorological phenomena such as wind shear andgust fronts in the vicinity of the airport. In addition, terminaldoppler weather radar (TDWR) may also be present at the airport toidentify the presence and locus of meteorological phenomena in theregion surrounding the airport to enable the air traffic controllers toguide the aircraft around undesirable meteorological phenomena such asthunderstorms. Additional data is available in the form of LANDSAT dataindicative of topological surface features surrounding the airport. Airtraffic control radar is also available to indicate the presence andlocus of aircraft within the space around the airport for air trafficcontrol purposes. Collectively, these systems provide datarepresentative of the immutable characteristics of the multidimensionalspace as well as the dynamic phenomena contained in the air space,including meteorological events and aircraft operations. It is notuncommon for airport operations to take place in a zero visibility modewherein the pilot's ability to obtain a visual image of air space infront of the aircraft is impaired to the point where the pilot is flyingblind. The pilot must rely on the air traffic controllers and radarcontained within the aircraft to ensure that the pilot does not fly theaircraft on a collision course with a solid object, such as anotheraircraft or the topological features surrounding the airport.

The virtual reality imaging system of the present invention converts thedata obtained from the multitude of systems and distills the informationcontained therein into a visualization of the flight path presently infront of the aircraft. This apparatus can delete extraneous information,such as clouds, fog, etc. and illustrate to the pilot and/or the airtraffic controller only phenomena that would be of significant interestto the pilot, such as dangerous meteorological phenomena and otheraircraft, to present the pilot with a clear image of hazards within themultidimensional space to permit the pilot to chart a course throughthese hazards without the pilot being able to see these dangers with thenaked eye.

The specific example noted above is simply one of many applications ofthis concept which operates to filter vast amounts of data typicallyfound in a visual imaging situation to present a "clearer image"to theuser as defined by the specific needs of the user. The user thereforesees only what they need to see and can complete tasks that heretoforewere impossible due to the visual overload encountered in manysituations, such as flying an aircraft through fog or clouds or notbeing able to identify a wind shear event in a meteorological phenomenaof significant extent and complexity. An additional capability of thissystem is the prediction of future states of the dynamic phenomena. Datais collected by the multitude of data acquisition systems over aplurality of sampling intervals and can be extrapolated to illustratethe state of the dynamic phenomena in future sampling intervals. Thiscapability enables the air traffic control supervisor to model theweather activity around the airport to provide information to planairport operations for the immediate future.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 illustrates in block diagram form the overall architecture of theapparatus of the present invention;

FIGS. 2-4 illustrate in flow diagram form the operation of the varioussegments of the improved weather alert system;

FIG. 5 illustrates in block diagram form the overall architecture of theimproved weather alert system;

FIG. 6 illustrates a plot of a typical airport configuration, includingLLWAS and TDWR installations and typical weather conditions;

FIGS. 7-12 illustrate an example of converting the compact datarepresentation of a phenomena to a three-dimensional objectrepresentation; and

FIGS. 13-17 illustrate typical visual images produced by this apparatus.

DETAILED DESCRIPTION

FIG. 1 illustrates in block diagram form the overall architecture of thevirtual reality imaging system 10 of the present invention. Within thevirtual reality imaging system 10, a data acquisition subsystem 1functions to collect and produce the real time data that isrepresentative of the multidimensional space and the features andphenomena extant therein. Graphics subsystem 2 functions to utilize thereal time data that is produced by the data acquisition subsystem 1 toproduce the visual displays required by the plurality of users. Toaccomplish this, a shared database 3 is used into which the real timedata is written by the data acquisition subsystem 1 and accessed by thevarious processing elements of graphics subsystem 2. A user data inputdevice 4 is provided to enable a user or a plurality of users to enterdata into the graphics subsystem 2 indicative of the particularinformation that each of the plurality of users desires to havedisplayed on the corresponding display device 11 operation, the dataacquisition subsystem 1 comprises a plurality of data acquisitionapparatus 21-2n, each of which produces data representative ofmeasurements performed on the phenomena or features that are located inthe multidimensional space. These data acquisition apparatus 21-2n canprocess the real time measurement data into compact data representationsof the phenomena and features, which compact data representations aretransmitted to graphics subsystem for processing into the visual images.The graphics subsystem 2 converts the compact data representationsproduced by the plurality of data acquisition apparatus 21-2n intovisualizations as defined by each of the users of the virtual realityimaging system 100. This visualization is produced by performing adatabase transversal to present the data in a form and format ofinterest to each of the users.

Aviation Weather Display System

A typical application of this apparatus is an aviation weather displaysystem whose data acquisition subsystems make use of a plurality ofaviation weather instrumentation that are used in and about an airportinstallation. The aviation weather instrumentation may include groundbased sensors such as radar, lighting detection networks, and windsensors as well as airborne sensors such as sounding balloons oraircraft based sensors. Each of the aviation weather instrumentationproduces raw data indicative of real time meteorological phenomena,topological features and aircraft operations in the multidimensionalspace, which real time data is processed by the data acquisitionsubsystem 1 to produce compact representations of the real time data.These data processing steps often include filtering, feature extraction,and correlation/integration of more than one data stream. Furthermore,this processed data may be used as input to physically based models,which attempt to predict the evolving phenomena based on the storedmeasurements.

From the compact data representations, the graphics subsystem 2generates generalized graphical representations of the phenomena andfeatures. This involves the creation of an object or objects which existin a virtual multidimensional space. In an aviation weather displayapplication, this virtual reality imaging system 10 must operate in realtime since significantly delayed data affects the validity andfunctionality of the system as a whole. The visualization presented tothe user typically includes frame of reference information such asterrain, overlaid with identifiable features in the form of highways,range rings or icons representing municipalities or airports.Furthermore, the terrain surface can be colored by texture mapping itwith an image such as a LANDSAT image or a digital map. In order tointegrate the plurality of data streams that are produced in a dataacquisition subsystem 1, the graphics subsystem 2 must perform numerousoperations such as database culling, relative level of detaildetermination and rendering to create user recognizable images from theraw data or compact data representations that are stored in database 3.

Data Acquisition Subsystem Architecture

FIG. 1 illustrates the major subcomponents of a typical data acquisitionapparatus 21. In a typical configuration, a plurality of sensors 201 areused to make measurements during a sampling interval of predeterminedduration and repetition frequency, of one or more characteristics of aparticular phenomena or feature within the multidimensional space. Theoutput signals from the plurality of sensors 201 are received by datafiltering and feature extraction element 202 which functions to filterthe data received from the plurality of sensors 201 to remove ambientnoise or unwanted signal components therefrom. The data filtering,feature extraction element 202 also functions to convert the raw datareceived from the plurality of sensors 201 into a definition of theparticular phenomena or feature that is being monitored by thisparticular data acquisition apparatus 21. An example of such acapability is the use of an improved low level wind shear detectionapparatus which converts the wind magnitude measurements from aplurality of ground base sensors into data representative of wind shearevents within the multidimensional space. To accomplish this, the rawdata obtained from the sensors 201 must be converted into a form toextract the wind shear events from the plurality of wind measurementstaken throughout the multidimensional space. The resultant informationis used by compact data representation apparatus 204 to produce a set ofdata indicative of the extracted feature in a convenient memoryefficient manner. This can be in the form of gridded data sets, featureextent and location data as well as other possible representations.Furthermore, the data acquisition apparatus can include a predictiveelement 203 which uses the data obtained from data filtering, featureextraction apparatus 202 to extrapolate into one or more predeterminedfuture sampling intervals to identify a future temporal state of thefeature or phenomena that is being measured. The data output by thepredictive element 203 is also forwarded to compact data representationelement 204 for inclusion in the data set that is produced therein. Theresultant compact data representations are transmitted to the graphicssubsystem 2.

It is obvious that if the feature being monitored is temporally andspatially static, the data that is produced is invariant and need not beupdated during successive sampling intervals. However, most phenomenathat are monitored in this environment tend to be temporally and in manycases spatially varying and the operation of the data acquisitionapparatus 1 is on a time sampled basis, with a set of data beingproduced at the end of each sampling interval. The plurality of dataacquisition elements 21-2n preferably operate in a time coordinatedmanner to produce synchronized sets of data sets in the database 3 sothat graphics subsystem 2 can produce temporally coordinated views ofthe phenomena and features located in the multidimensional space on aonce per sampling interval basis or over a plurality of samplingintervals, dependent on the amount of data that must be processed. In areal time environment, the plurality of data acquisition apparatus 21-2nfunction to collect tremendous amounts of data and reduce the data tomanageable amounts for use by the graphics subsystem 2.

The improved low-level wind shear alert system, illustrated in blockdiagram form in FIG. 5, provides an improved method of identifying thepresence and locus of wind shear in a predefined area. This low-levelwind shear alert system enhances the operational effectiveness of theexisting LLWAS system by mapping the two-dimensional wind velocity,measured at a number of locations, to a geographical indication of windshear events. This resultant geographical indication is displayed incolor-graphic form to the air traffic control personnel and can also betransmitted via a telemetry link to aircraft in the vicinity of theairport for display therein. In addition, gust fronts are tracked andtheir progress through the predefined area displayed to the users.

This low-level wind shear alert system can also integrate data andprocessed information received from a plurality of sources, such asanemometers and Doppler radar systems, to produce low-level wind shearalerts of significantly improved accuracy over those of prior systems.In particular, the apparatus of the improved low-level wind shear alertsystem makes use of the data and processed information produced by theexisting Low-Level Wind Shear Alert System (LLWAS) as well as thatproduced by the Terminal Doppler Weather Radar (TDWR) to preciselyidentify the locus and magnitude of low-level wind shear events within apredetermined area. This is accomplished by the use of a novelintegration system that utilizes the data and processed informationreceived from these two systems (LLWAS & TDWR) in such a way that thelimitations of the two stand-alone systems are ameliorated. Thisintegration scheme, while addressing these limitations, simultaneouslymaintains the strengths of the two stand-alone systems. This techniquethen provides the best possible wind shear hazard alert information.Furthermore, this integration methodology addresses the operatorinteraction problem discussed above. The integration is fully automated,requires no meteorological interpretation by the users and produces therequired graphical and alphanumeric information in an unambiguousformat. Lastly, this integration technique is implemented fully withoutany major software modifications nor without any hardware modificationsto the existing stand-alone systems.

The TDWR apparatus uses a 5 cm. C-band Doppler radar system to measureradial winds when atmospheric scatterers are present. This systemprocesses the radar return signals to create a field of radiallyoriented line segments indicative of the radial velocity data receivedfrom the radar. The TDWR apparatus bounds isolated sets of segments thatare above a predetermined threshold to define an area which wouldcontain a specific, potential low-level wind shear event. The boundingis such that it incorporates the smallest area which includes all of theline segments above the predetermined threshold. A predefined geometricshape is used to produce this bounding and the characteristics of thisgeometric shape are adapted in order to encompass all of the requireddata points in the minimal area.

The apparatus of the improved low-level wind shear alert system isdivided into two independent sections: detection of wind shear with losssituations (microbursts, etc.) and detection of wind shear with gainsituations (gust fronts, etc.). The TDWR system outputs wind shear withloss data in the form of microburst shapes. The enhanced low-level windshear alert system generates equivalent LLWAS microburst shapes usingthe triangle and edge divergence values produced by the existing LLWASapparatus. The LLWAS microburst shapes are validated by using auxiliaryinformation from LLWAS and TDWR to eliminate marginal andfalse-detection LLWAS microburst shapes. The resultant two sets ofmicroburst shapes are then considered for alarm generation purposes. Thewind shear with gain portion of this system simply divides the coveragearea into two regions, with TDWR producing wind shear with gain runwayalarms for wind shear events that occur outside of the LLWAS sensorwhile the LLWAS runway oriented gain alarms are produced for wind shearevents that occur inside of the LLWAS sensor network.

This integration architecture enables the concurrent use of a pluralityof sensor-based systems to provide the wind shear detection functionwith increased accuracy. Both ground-based and aircraft-based sensorsystems can be used to provide wind data for this apparatus. The mappingof diverse forms of input data into a common data structure (predefinedgeometric shapes) avoids the necessity of modifying existing sensorsystems and simplifies the production of information displays for theuser. The use of a common information display apparatus and formatrenders the combination of systems transparent to the user.

Improved Low-Level Wind Shear Detection System

Adverse weather conditions, especially those affecting airportoperation, are a significant safety concern for airline operators. Lowlevel wind shear is of significant interest because it has caused anumber of major air carrier accidents. Wind shear is a change in windspeed and/or direction between and two points in the atmosphere. It isgenerally not a serious hazard for aircraft en route between airports atnormal cruising altitudes but strong, sudden low-level wind shear in theterminal area can be deadly for an aircraft on approach or departurefrom an airport. The most hazardous form of wind shear is themicroburst, an outflow of air from a small scale but powerful downwardgush of cold, heavy air that can occur beneath or from the storm or rainshower or even in rain free air under a harmless looking cumulus cloud.As this downdraft reaches the earth's surface, its spreads outhorizontally like a stream of water sprayed straight down on a concretedriveway from a garden hose. An aircraft that flies through a microburstat low altitude first encounters a strong headwind, then a downdraft,and finally a tailwind that produces a sharp reduction in air speed anda sudden loss of lift. This loss of lift can cause an airlane to stalland crash when flying at a low speed, such as when approaching anairport runway for landing or departing on takeoff. It is thereforedesirable to provide pilots with a runway specific alert when a fifteenknot or greater headwind loss or gain situation is detected in theregion where the aircraft are below one thousand feet above ground leveland within three nautical miles of the runway ends.

FIG. 6 illustrates a top view of a typical airport installation whereinthe airport is within the region indicated on the horizontal axis by theline labeled L and a Terminal Doppler weather Radar system 502 islocated a distance D from the periphery of the airport. Included withinthe bounds of the airport are a plurality of Low Level Wind Shear AlertSystem sensors 505. The sensors 505 are typically anemometers locatedtwo to four kilometers apart and are used to produce a single plane, twodimensional picture of the wind velocity within the region of theairport. The Terminal Doppler Weather Radar 502, in contrast, consistsof a one dimensional (radial) beam which scans all runways (R1-R4) andflight paths but can measure only a radial horizontal outflow componentof wind. The nominal TDWR scan strategy produces one surface elevationscan per minute and scans aloft of the operational region to an altitudeof at least twenty thousand feet every two and a half minutes. Thisstrategy is intended to provide frequent updates of surface outflowwhile monitoring for features aloft to indicate that a microburst isimminent. Microbursts (M1-M8) are recognized primarily by surfaceoutflow although they can be anticipated to a certain extent bymonitoring features and events in the region above the airport location.

Thunderstorms typically produce a powerful downward gush of cold heavyair which spreads out horizontally as it reaches the earth's surface.One segment of this downflow spreads out away from TDWR radar while anopposing segment spreads out towards the TDWR radar. It is generallyassumed that these outflows are symmetrical for the purpose of detectingmicroburst wind shears. Because most microbursts do not have purelysymmetrical horizontal outflows, the TDWR system can have problemsdetecting or estimating the true intensity of asymmetrical microburstoutflows. As can be seen from FIG. 6, the anemometers 505 of the LowLevel Wind-Shear Alert System are sited on both sides of airport runwaysR1-R4 but do not extend to the full three mile distance from the end ofthe runway as is desirable. Therefore, the anemometers 505 can onlydetect horizontal airflows that occur in their immediate vicinity (M2,M3, M5-M8) even though there can be horizontal airflow outside theanemometer network (M1, M4) that can impact airport operations but areoutside of the range of the limited number of anemometers 505 sited atan airport.

Improved Wind Shear Alert System Architecture

FIG. 5 illustrates in block diagram form the overall architecture of theimproved low-level wind shear alert system 100. This low-level windshear alert system 100 integrates the ground level wind data collectedby one set of stationary ground level sensor (anemometers) 505 with thehigher altitude wind data collected by a second sensor (Doppler radar)502 in order to accurately identify both the locus and magnitude oflow-level wind shear conditions within a predetermined area A. The twosets of data inputs illustrated in this embodiment of the inventioninclude the data produced by existing data processing systems associatedwith the sensors in order to preprocess the data prior to integrationinto the unified precise output presented to the end user.

The sensor systems include the existing Low Level Wind Shear AlertSystem (LLWAS) front end processing 101 which is an anemometer-basedwind shear alert system used to detect the presence and identify thelocus of wind shear events at or near ground level. The LLWAS system 101generates data indicative of the wind velocity (magnitude and direction)at each of a plurality of fixed sites 505 located within a predefinedarea. The collected wind velocity data is then preprocessed by the LLWASsystem 101 to identify the locus and magnitude of wind shears at groundlevel by identifying the divergence or convergence that occurs in themeasured wind velocity throughout the predefined area. Similarly, thesecond set of sensors is the Terminal Doppler Weather Radar (TDWR) 502which uses a Doppler radar system to measure low-level wind shearactivity in the predefined area. The TDWR system 502 searches its radarscan for segments of the radar beam of monotonically increasing radialvelocity. These regions and areas of radial convergence are identifiedas the locus of wind shear events.

The integration system 103 that has been developed for the integrationof TDWR 502 and LLWAS 101 uses a product-level technique and is dividedinto two independent sections: the detection of windshear-with-losssituations (microbursts, etc.) and windshear-with-gain situations (gustfronts, etc.).

The outputs from the windshear-with-loss portion of the TDWR system 502are microburst shapes--which are used both as graphical information andto generate the textual runway alerts. As an integration "add-on" to theexisting LLWAS system 101, an enhanced LLWAS section 102 was developedto generate LLWAS microburst shapes. These shapes are computed usingtriangle and edge divergence values obtained from the LLWAS system 101.Even though the methods used to generate these shapes is quitedifferent, these LLWAS microburst shapes are identical--in both form andcontent--to the TDWR microburst shapes. This allows for the samealert-generation logic to be applied, and for the common graphicaldisplay 116 of microburst detections.

The TDWR/LLWAS (windshear-with-loss) microburst integration 114 isessentially the combined use of microburst shapes from each sub-system112,502. This combination, however, is not a spatial merging of theshapes: each shape is considered as a separate entity. Furthermore, theLLWAS microburst shapes have been passed through a validation process insymmetry test 113. By this we mean that auxiliary information 703 fromboth TDWR and LLWAS is utilized in an attempt to eliminate certain ofthe "weaker" LLWAS microburst shapes--ones that could generate nuisanceor false alarms. The motivation and implementation for this procedure isdescribed below. However, an alternative to this process, the sensordata from each of the subsystems 112, 502 could be merged to produce acomposite set of shapes indicative of the merged data. This alternativeprocess is noted herein in the context of this system realization.

Once a set of microburst shapes are produced by the enhanced LLWASapparatus 102 and integration apparatus 103, these shapes aretransmitted to the Terminal Doppler Weather Radar system 502 whichcontains the runway loss alert generation process. Similarly, theintegration apparatus 103 receives LLWAS runway oriented gain data andTDWR gust front data in gust front integration apparatus 115. The LLWASrunway-oriented-gain data includes data front tracking system 119 whichuses the LLWAS anemometer wind vectors to detect, track, and graphicallydisplay gust-fronts within the predetermined area. LLWASrunway-oriented-gain (ROG) is also used for detection of generic windshear with gain hazards within the LLWAS network. This is notnecessarily tied to a specific gust front detection. Wind shear withgain situations can occur independently of gust fronts e.g. the leadingedge of a microburst outflow, or larger-scale (meteorological) frontalpassage. The selected data is then transmitted to the TDWR system 505where a runway gain alert generation process produces an alarmindicative of the presence of a wind shear with gain hazard.

Alarm arbitration process in TDWR system 502 selects the alarm producedby either runway loss alert generation process or runway gain alertgeneration process to present to TDWR displays 116. The existingdisplays 116 consist of the TDWR Geographic Situation Display (GSD)which illustrates in graphical form the microburst shapes, gust frontsand indicates which runways are in alert status. The TDWR and LLWASRibbon Display Terminal (RDT) gives an alphanumeric message indicatingalert status, event type, location and magnitude for each operationalrunway.

It is obvious from the above description that the existing LLWAS 101 andTDWR 502 systems are utilized as much as possible without modificationto minimize cost and impact on existing installations. It is alsopossible to implement these features in other system configurations. Anyother data collection system can be similarly integrated with theexisting TDWR system 502 or the existing LLWAS system by the applicationof the philosophy described above. For example, the addition of anotherDoppler radar, or another anemometer network.

Shape Generation Philosophy

The LLWAS microburst shape computations are based upon the detection ofdivergence in the surface winds. These triangle and edge divergenceestimates are mapped onto a rectangular grid. Contiguous "clumps" ofabove-threshold grid points are collected and then used to generatemicroburst shapes. Compensating for the spatial under-sampling of thetrue surface wind field inherent in the LLWAS data, a "symmetryhypothesis" is used in generating the location, extent, and magnitude(loss estimate) for these microburst shapes. This hypothesis is appliedas if a symmetric microburst were centered at each (above threshold)grid point. In general, microburst outflows are not symmetric. However,the spatial superposition of these symmetric "grid-point-microbursts" ina given clump does a very good job of approximating a non-symmetricevent.

While a given detected divergence may be real, the LLWAS data alonecannot be used to determine whether it is truly associated with amicroburst. Therefore, the application of the symmetry hypothesis maynot always be valid. The problem is two-sided. If the symmetryhypothesis is always used, it could generate false alarms in certainnon-microburst situations. For example, strong surface winds setting upin a persistent divergent pattern. On the other hand, if the symmetryassumptions are never used, wind shear warnings for valid microburstevents could be delayed, inaccurate, or even eliminated. The issue isthen to determine whether a given LLWAS-detected divergence isassociated with a microburst and hence determine whether the symmetryhypothesis should be applied.

The algorithm that was developed combined "features-aloft" informationfrom TDWR: three-dimensional reflectivity structures and microburstprecursors, (both projected down to the surface); and detected "strong"surface divergence (microburst shapes) from both TDWR 502 and LLWAS 101.This information is then synthesized, both spatially and temporally tocreate a set of geometric discs. The intent of these discs is toindicate a region of the atmosphere within and/or above the disc, (i.e.a cylinder), where there is good likelihood of microburst activity. This"region" could be in space: the detection of the surface outflow, ormicroburst features above the surface (reflectivity and/or velocitysignatures). It could also be in time, that is, a microburst is either:going to occur, is in progress, or has recently been present.

These discs are then examined for "closeness" to those LLWAS microburstshapes that are to be validated. If this proximity criteria is met, theLLWAS microburst shape is "validated" and passed onwards. That is, theuse of the symmetry hypothesis is assumed to be appropriate in thiscase, and this LLWAS microburst shape is to be used for generating windshear warnings and to be displayed on the GSD. If the proximity testfails, the LLWAS shape is discarded. However, in this lattercircumstance, there could be a valid wind shear hazard occurring that isnot associated with a microburst--or possibly a microburst that is notbeing correctly identified in the symmetry disc calculations. To preventthis type of missed detection, the LLWAS Runway-Oriented-Loss (ROL)information 703 is then used as a fall-back to generate any appropriatewind shear warnings.

Enhanced LLWAS System-Preprocessing

The enhanced LLWAS system creates a grid point table for use in creatingmicroburst shapes. This process is illustrated in FIG. 3 and isactivated at system initialization. As a preprocessing step, a set ofpointers are generated which map triangle and edge microburst detectionareas to an analysis grid. During real-time operation, LLWAS triangleand edge divergence values are then mapped onto the grid applying amagnitude value at each grid point. This set of grid point magnitudesare used with the clumps produced by clump shape generation apparatus111 to generate a set of low level wind shear alert system microburstshapes. The "pointers" for the mapping of triangle and edges to the gridis a "first-timethrough", preprocessing step. This is done this waysince the "pointer" information is solely a function of a given site'sLLWAS anemometer network geometry--which doesn't change.

The preprocessing, location specific table data generation is initiatedat step 1201 where the anemometer location values are retrieved frommemory and, at step 1202 the site adaptable parameters needed to modifythe calculations are also retrieved from memory. At step 1203, a grid iscreated by computing the number of grid points in an x and y Cartesiancoordinate set of dimensions based on the number of input data points tocreate a minimal size xy grid to perform the computations. At step 1204,a set of grid pointers is produced to map the divergence estimates thatare above a threshold value with the particular points in the gridsystem created at step 1203. This is to locate the center of amicroburst that would be causing an alarm. Since a number of grid pointsare above the divergence element threshold value it is difficult todenote the location where the microburst to be centered which wouldcause these elements to create the alarm. Each sensor or network elementis tested by placing a mathematical microburst at each grid point andeach one of the grid points so tested that would cause the given networkelement to be an alarm status is then associated with that particularnetwork element. As a result, a set of grid points associated with eachLow Level Wind Shear Alert System 101 triangle and edge is produced tocreate the element grid point pointers. In order to perform thiscalculation, a symmetrical microburst model is used: a simplistic halfsine wave model which is time invariant and symmetric in both space andmagnitude and is only a function of amplitude and a maximum radius. Eventhough a real microburst may be spatially asymmetrical, it can beapproximated by a linear superposition of a number of symmetricalmicrobursts at least to a first order mathematical expansion whichproduces sufficient specificity for this calculation process.

Once the above steps have been performed, the processing of measurementdata begins at step 1205, where the Low Level Wind Shear Alert Systemtriangle and edge divergence values are used to generate thecorresponding sets of ratios of the divergence values to the thresholds,estimated loss values and alarm status. Associated with these gridpoints are two sets of magnitude values: the low level wind shear alertsystem divergence to threshold ratios and associated estimated lossvalues. The purpose of these two sets of magnitude information lies inthe fact that, although the measured quantity is wind-field divergence(or windshear), the required output value to the users is arunway-oriented loss value. Hence a mapping from divergence to loss isneeded.

The following data processing steps are done at each update ofinformation from the LLWAS system:

1. Input of triangle and edge divergence values from LLWAS system.

2. Computation of "ratios" (divergence/threshold) for each triangle andedge.

3. Mapping of triangle and edge ratios to grid.

4. Clumping of grid points.

5. Shape generation from clumps.

Clump Generation Theory

FIG. 2 illustrates in flow diagram form the clump generation process 111which receives algorithm products from the Low Level Wind Shear AlertSystem 101 to produce an indication of the location of wind shearevents. This routine accepts as an input the triangle and edgedivergences produced by the Low Level Wind Shear Alert System 101. Theclump generation process 111 then generates clumps of points that areabove a certain input threshold level. These clumps are then output tothe low level wind shear alert system shape generation algorithm 112.The grid points are the data collection points within the predefinedarea around the airport which area is presumed to be two dimensionalrectangular area having a set of coordinates in the standard twodimensional rectilinear mathematical orientation with positive x valuesto the east and positive y values to the north. The clumps are generatedby first finding grid points that are above a given threshold value.

In the pre-processing stage, a grid with 0.5 km by 0.5 km spacing isconstructed over a region which covers the anemometer network 505. Asimulated microburst is placed at each grid point and the divergence iscomputer for each network element. If the computer divergence for agiven element is above that element's threshold, an "association" ismade between the grid point and that element. In this manner, a table isconstructed that connected all of the grid points to the networktriangles and edges via a hypothetical divergence detection. This tableis then employed in real-time using an inverse logic. Given that anetwork element detects a divergence above its threshold, a set of gridpoints (via the table) is associated with that divergence, since fromthe theoretical analysis these points are potential microburstlocations.

Once these subsets of grid points have been identified, they areprocessed to generate "clumps" of contiguous groups of grid points. Bycontiguous, it is meant that adjacent up, down, right, or left pointsare considered, not those along the diagonals. Three sets of clumps aregenerated to include grid point threshold data representative of"low-level", "high level", and "low-level-density" collections of gridpoints. The "low-level" and "high-level" grid points are indicative ofthe magnitude of the estimated wind divergence at those particular gridpoints. The "high-level" grid points are representative of a secondarythreshold used to distinguish the grid points that have significantlyexceeded the initial threshold. This secondary threshold therebydifferentiates wind shears of significant magnitude from those ofmoderate magnitude.

"Low-level-density" grid-point clumps are identical to those for the lowlevel and high level process discussed above but represent acondensation of a large number of grid points, which number would beoverly large or the resultant geometric pattern would be concave orextended in nature. Art example of such a problem would be a collectionof grid points that are located in a figure eight shape. In order toreduce the collection of grid points into small, convex and compactpatterns, a density weighing operation is performed on the low levelgrid point values. In order to accomplish this, the original magnitudeof each grid point is multiplied by a local neighborhood occupationdensity weight to compute a new magnitude value at each grid point tothereby more accurately reconfigure the geometric pattern of these gridpoints. The density weight is a normalized value between zero and onewhich is generated by any one of a number of mathematical methodsdepending upon a given point's location in the grid. For example, theneighborhood set of points for a given interior point are the eightadjacent points including the diagonals and the given point itself. Thenumber of points in this set that are above a threshold value are summedand this total number is divided by the number of grid points that arein the original neighborhood set. These density weighted points are thenformed into clumps in an identical fashion as for the low level and highlevel computations to form the low level density geometric clumps. Thisprocedure condenses the collection of grid points into more compactpatterns and also separates overly extended clumps into a set ofsmaller, compact clumps.

Preferred Geometric Shape

A single preferred geometric shape is used throughout these computationsin order to have consistency and simplicity of the calculations. Thepreferred shape disclosed herein is a semi-rectilinear oval akin to theshape of a band-aid, that is a rectangle with semi-circle "end-caps"(these microburst shapes are the same as the TDWR shapes). This shape ismathematically defined by an axis line segment having two end points anda radius used at each of the end points to define a semicircle. Thisgeometric shape is produced for each clump such that the axis linesegment has the minimum weighted squared distance from all of the gridpoints that are within this given clump and furthermore, this shapeencloses all of the clump's grid points. In cases where the shape isoverly large or concave in nature, the shape is processed to create anumber of smaller shapes which enclose the grid points. This shape isproduced such that it is of minimum area after satisfying theseconditions. A further processing step, a least-squares size reduction,is then performed to "trim" overly large shapes. In computing the shapesfor microbursts, the magnitude information used is the ratio of thecalculated divergence to the threshold that is mapped from triangles andedges into the grid points. A given grid point's ratio value isgenerated as follows. First, a ratio for each LLWAS network element:(triangle and/or edge), is computed. This ratio is the ratio of thatelements' detected divergence estimate and that elements' divergencethreshold value. This predetermined threshold is designed to indicatehazardous wind-field divergence, is computed based upon a mathematicalmicroburst simulation, and takes into account the geometrical nature ofthe given triangle or edge. Another set of magnitude information used isan associated loss value estimate for each point, based on thesedivergences. The microburst shapes are calculated at the "wind shearalert" (WSA) level using the low level density clumps, least squaresshape size reduction and the statistical shape magnitude computation.The other set of geometric shapes is at the "microburst alert" (MBA)level using the high level clumps, least squares reduction and themaximum value of magnitude computation.

Clump Generation Process

FIG. 2 illustrates in detailed flow diagram the clump generation process111 which process is initiated at step 1102 where the data is receivedfrom the associated low level wind shear alert system 101 and stored inmemory. At step 1102, the clump generation process 111 converts the lowlevel magnitude points into local occupied neighbor density weightedmagnitude values. This process as discussed above uses all of the lowlevel input magnitude values and computes new values for these pointsbased on the density of adjacent data points that have exceeded theinitial predetermined threshold. Each given data point that is above theinput threshold value is given a density weight which is a numberbetween zero and one indicative of the number of contiguous grid points,including the given point that are above the input threshold value,divided by the total number of contiguous points. That is, for aninterior point the density weight is the number of neighboring pointsabove the input threshold value divided by nine. This is because thecontiguous points is defined as the adjacent points to the left, right,up, down and the four diagonal points in this xy Cartesian coordinatesystem. Once this set of density weighted values have been computed,processing advances to step 1104 wherein the initial groupings of datapoints is accomplished by grouping the grid points that have exceededthe threshold value into contiguous groupings. Concurrently with theoperations on low level density data points, or subsequent thereto, thesteps 1105 and 1106 are executed on the high level magnitude points toperform the same contiguous grouping function of steps 1102 and 1103.The set of groupings is then used at step 1106 by the shape driver togenerate the predetermined geometric shapes of minimum area.

Using points that are still inside the shape after radius reductioncompute the least squares reduced axis segment to produce a new reducedaxis line segment. The resultant reduced shape axis line segment is thenconverted into the original, non-rotated Cartesian coordinate system andthe overall magnitude for the shape is computed. The resultant shapeconsists of a line whose end points represent the center of a semicircleof predetermined radius which end point semicircles when connected bystraight line segments create a band-aid shape to enclose all of thedata points in a minimal area whose magnitude has been calculated.Similar processing of the input data takes place for the high levelmagnitude points in steps 1106 and 1107 the processing of which canoccur sequentially or in parallel with the operation of steps 1104 and1105. Once the shapes and their magnitude have been calculated for boththe low level density magnitude points and the high level magnitudepoints processing exits at step 1109.

Shape Production

As noted above, this predetermined geometric shape is a band-aid shapewhich is defined by an axis line segment having two end points and aradius used at the end points to produce two semicircular shapes. Thisprocess is illustrated in flow diagram form in FIG. 3. The process isinitiated by retrieving all of the grid points in one of the above notedsets and storing these in memory. Using these stored grid points, themeasured or calculated magnitude of each grid point in a clump isnormalized. Once all of the grid point values in the set have beennormalized, a weighted least squares line is fit through these pointsusing a standard weighted least squares technique. This produces thebest line fit through all of the valid points in the input set of gridpoints. Once the weighted least squares line has been produced, the endsof this line segment are calculated by projecting all of the data pointsin the set onto the computed least squares line. The process uses thecoordinates of each of the data points and the slope of the computedleast squares line through these points. The coordinates of the clumppoints are put into a rotated coordinate system such that the leastsquares line is horizontal. The output from this calculation is theclump point coordinates in this rotated system and the axis line segmentend points also in this coordinate system. The first set of coordinatevalues of this rotated end point is the leftmost point on the linerepresentative of the smallest x value in the rotated xy Cartesiancoordinate system and the second coordinate output is the rightmostpoint representative of the largest x value in this Cartesian coordinatesystem. Once the ends of the shape line segment have been determined allof the subsequent computations are done in the rotated coordinatesystem. The radius of the shape that encloses the points and is ofminimum area is calculated by using a one dimensional smooth-function,(i.e., monotonic) minimization routine.

Shape Area Minimization

The minimization function is then activated to compute the radius thatminimizes the shape area and using this new radius a review is made todetermine whether the axis line segment end points can be modified inview of the determined radius. This is done by projecting the valid datapoints in the current set onto the computed least squares line andcomputing new end points as discussed above. Once this is done, the axislength is reduced if possible by moving the axis end points towards theaxis segment bary center using a weighted least squares reduction of thehorizontal distance from clump points to the closest shape boundary. Byclosest, it is meant that these points are partitioned into three sets:a set whose x values are less than the shapes bary center, a set whose xvalues are greater than the shapes bary center and a set of points thatwere originally associated with the shape but after radius reduction arenow outside the shape. The normalized weights are selected to be afunction of points magnitude and its distance to the axis segment barycenter. The process uses the current access line segment end points andcomputes the bary center of the current axis line segment andinitializes the minimization iteration interval.

If the shape so generated is too large, it is dissected into a pluralityof shapes. The test of excessive size is that the length of the axisline segment plus twice the radius is greater than a predeterminedthreshold. If so, the axis line segment is divided into smaller andpotentially overlapping pieces. The grid data points originallyassociated with the original clump are then associated with thecorresponding subshapes. If there is an overlap of the multiple shapes,the grid data points can be associated with more than one shape. Theresultant plurality of shapes more accurately reflect the concurrentexistence of multiple adjacent or overlapping wind shear events.

Least Squares Shape Size Reduction

This process provides for a simple, efficient and mathematicallyrigorous method for more precisely indicating the hazardous microburstregion. The original microburst shape algorithm--still used in the TDWRsystem, requires that all of the shear-segments 804 (the "runs of radialvelocity increase") be enclosed within the microburst shape(s) 803.(FIG. 8) If the locus of these shear segments 804 is overly extendedand/or fairly concave in geometrical structure, the "all enclosing"shape 803 can be too large. That is, it may contain nonhazardous regions805. This can generate false alarm warnings as a runway alarm isgenerated when any portion of a microburst shape 803 intersects apredefined box 802 around a given runway 801. This same situationapplied with the LLWAS microburst shapes. Where herein, we are concernedwith overly extended and/or concave grid point clumps, as opposedshear-segment clusters, though the concept is identical. The solution tothis documented "overwarning" problem has been developed in the contextof the least-squares reduction of the shape-size for the LLWASmicroburst shapes in the apparatus of the present invention.

A further contribution of the "overwarning" problem, is in thegeneration of the "magnitude" of the runway alert. That is, after agiven microburst shape 803 intersects a "runway alert-box" 802, amagnitude for the alert must be computed. Again, the technique used forthe TDWR stand-alone system is fairly simplistic and tends toover-estimate the hazard magnitude. These over-estimates are oftenviewed as false-alarms by the pilots. Therefore, again in the context ofthe LLWAS microburst shapes, a simple, efficient and mathematicallyrigorous methodology is used in the apparatus of the present invention.This algorithm employs a statistical estimate for a given microburstshape's magnitude.

A shape is defined by two axis end points: (X_(e1), Y_(e1)) and X_(e2),Y_(e2)), [X_(e1) ≦X_(e2) ] and a radius R. (FIG. 7) The shape isgenerated initially by finding the line which, in a least squares sense,(weighted by magnitude) best fits the set of points in a given "clump".These clump points essentially reflect the divergence magnitude at thosepoints in space--as estimated from the LLWAS wind field.

The radius is then found by an iterative procedure which minimizes thearea of the shape while simultaneously requiring that all points in theclump are enclosed. This technique is identical to the procedure usedfor TDWR, which uses "segment endpoints" as opposed to "points in aclump". Next, we try to reduce the shape size so that it gives a betterfit to the points. This is done because the original criteria that allpoints be enclosed, tends to result in overly-large shapes when theclump is fairly concave. A further undesired complication occurs becauseof the generally "weaker-magnitude" points on the edges of the clump.This can be conceptualized by considering a symmetrical microburstoutflow. The clump points can be viewed as describing contour-levels ofdivergence. The "center" of the clump being the "center" of themicroburst outflow. The highest level of divergence would be at thecenter of the microburst outflow, then monotonically decreasing inmagnitude with increasing distance from the center. The shape's radiusis first reduced, then the axis length. Both are done using a weightedleast squares technique.

Reduction of the Shape Radius

What we do here is reduce the (weighted) distance of the (originally)enclosed points, (X_(k), Y_(k)), to the shape boundary.

We have that R=d_(k) +d_(k), where R is the original radius, d_(k) isthe perpendicular distance from the point to the shape axis (or axisendpoint if X_(k) ≦X_(e1), or X_(k) ≧X_(e2)), and d_(k) is the distancefrom the point to the boundary.

Therefore, we minimize d_(k) =R-d, which leads to the weighted leastsquares equation for R, the new radius: ##EQU1## which has the solution:##EQU2## when we choose a set of normalized weights W_(i), ΣW_(k) =1.

We define the weights to be: ##EQU3## where m_(k) is the given magnitudeat each point. This weighing is used to remove the bias generated by therelative higher density of the internal points. This can be understoodby considering a shape which is a disc, and whose constituentclump-points all have equal magnitudes. If the weighing function onlyconsidered magnitudes, then the least squares radius reduction wouldalways attempt to make a new disc of minimal-radius. The use of thedistance values in the weighing function is designed to counteract thistendency. Furthermore, we choose a coordinate system rotated such thatthe axis is horizontal.

    Y.sub.1.sup.* =Y.sub.1.sup.* ≡Y,X.sub.K →X.sub.k.sup.*, Y.sub.K →Y.sub.k.sup.*

(* indicating rotated coordinates)

In this coordinate system, the dk's are given by: ##EQU4##

Reduction of the Shape Axis Length

Next, we reduce the axis length by (separately) moving the axis segmentendpoints toward the segment mid-point. We use a least squares reductionof the horizontal (in rotated coordinates) distance from a given pointto the (closest) boundary. Note: the axis is reduced only when the axislength is longer than a threshold length (approximately 1 km). By"closest", we mean that the clump points are partitioned into threesets: a set whose X-coordinates are less than the shape axis segment'smid-point, X; one "greater-than" X; and a third set consisting of thosepoints that (after radius reduction) are outside the shape. We do notuse this third set of points since their (horizontal) distance to theboundary is (now) undefined. ##EQU5##

Therefore, the problem we are trying to solve (for a generic endpoint"e") is:

    d.sub.k =d.sub.k- (X.sub.e -X.sub.e)

where d_(k) is the horizontal (X^(*)) distance from point k to theboundary; d_(k) is the (eventual) least squares distance; X_(e) andX_(e) are similarly the original and least squares endpoints.

The new endpoint we want is:

    X.sub.e =ΣW.sub.j (d.sub.j -X.sub.e)

where the set of points j refers to either points greater than X for the"right" endpoint or less than X for the "left" endpoint, respectively.The weights are chosen to be: ##EQU6## where:

    ΣW.sub.j =1

As before, the weights are chosen to reduce over-bias by points close toX.

The horizontal (X)-distance to the boundary d_(J) is given by: ##EQU7##The value we want to minimize is then:

    d.sub.j -X.sub.e.sup.* =(R.sup.2 -Y.sub.j.sup.*2).sup.1/2 -X.sub.j.sup.*,

where L_(j) is the horizontal distance from the point (X_(e) ^(*), Y_(j)^(*)) to the least squares reduced boundary, and is the horizontaldistance between X_(j) ^(*) and X_(e) ^(*) :

    L.sub.j =(R.sup.2 -Y.sub.j.sup.2).sup.1/2

(e,otl R× is the least squares reduced radius)

    ΔX.sub.j =X.sub.j.sup.* -X.sub.e.sup.*

Therefore, the new endpoint, X_(e) is given by (again in rotatedcoordinates):

    X.sub.e =ΣW.sub.j [(R.sup.2 -Y.sub.j.sup.*2).sup.1/2 -X.sub.j.sup.* ]

where: ##EQU8## Note: the same values result for points between X andX_(e) ^(*), and X_(e) ^(*) and the boundary. Furthermore, the sameresult applies to points on either side of X. That is, the sameequations apply equally for both sets of points "j" (partitioned basedupon being less-than or greater-than X).

LLWAS Microburst Shapes--Magnitude Computation

This routine computes an overall magnitude estimate for a given shape.The technique is to assume a Student's t-statistic distribution for themagnitudes for the set of points associated with the shape. The shapemagnitude is then the percentile value given by the mean magnitude plus"K" standard deviations. This is an application of the well-known"confidence interval" technique from statistical theory. Thisdistribution was chosen for its applicability to small sample sets andits approximation to a normal distribution for sample sets of aroundthirty elements or more. Furthermore, the value of "K" that has beenused (k=1.0), was chosen to approximate an 80 to 90^(th) percentilevalue over a wide range of degrees of freedom, (which is the number ofpoints minus one).

Symmetry Test

Symmetry test apparatus 113 validates the microburst shapes produced bymicroburst shapes generator 112 based on the auxiliary informationproduced by the features aloft and shape information obtained from theTerminal Doppler Weather Radar System 502. This validation determines ifthere is supporting evidence that a given LLWAS microburst shape, istruly associated with a microburst. That is, the shape that is generatedfrom the detection of surface wind field divergence can be associatedwith either a microburst or some other type of wind field anomaly, suchas thermal activity, noisy winds, etc. Since symmetry assumptions areimplicit in a generation of microburst shapes and these assumptions arebased on the association of the surface divergence with the microburst.In non-microburst situations, these assumptions can lead to thegeneration of unwanted false alarms. This symmetry test procedure 113removes the unwanted alarms by reviewing reflectivity and microburstprecursor information from the Terminal Doppler Weather Radar system502. These inputs are combined spatially and temporally to form symmetrydisks whose presence indicates the possible existence of a microburstwithin or above its boundary. The given microburst shape that is to bevalidated by the symmetry test 113 is then tested for its proximity to asymmetry disk. Therefore, a weak microburst shape that is close to asymmetry disk is validated and those that are not are presumed to be anerroneous detection.

This symmetry test 113 is initiated at step 1301 with retrieval of sitespecific parameters from memory to modify the calculations based onlocal climatological conditions and sensor configuration. At step 1302,a rectangular grid in the xy Cartesian coordinate system is producedconsisting of a minimal size grid necessary to analyze the calculatedshapes. At step 1303 the microburst shapes are selected whose magnitudeare equal to or greater than a site adaptable threshold. At step 1304the present grid point values are computed based on current TerminalDoppler Weather Radar features aloft information and any TerminalDoppler Weather Radar or Low Level Wind Shear Alert System microburstshapes. The features aloft inputs are in the form of disks described byan xy center coordinate, a radius, and a type: low reflectivity, stormcell, reflectivity core or microburst precursor disks. A magnitude valuefor each of these features aloft disks is assigned based upon its type.The microburst shapes herein are those that have been filtered outprevious to this routine and exceed the predetermined threshold values.Therefore, all of the Low Level Wind Shear Alert System and TerminalDoppler Weather Radar shapes computed are screened to come up with acomposite set of shapes that exceed a given threshold value. For eachdisk that impacts the analysis grid that has been produced, specificgrid points within that disk have their magnitude updated based on thenature of the disk. Each grid point magnitude value is time filteredwith a single pole recursive filter to enforce a sense of timecontinuity. This set of filtered magnitudes is then the output of thisroutine to the create symmetry disks step 1305. The disk magnitudes areselected by appropriately choosing base or minimal values for each inputset so that the features aloft disk type relates to the value of theactual loss magnitudes. Once these grid values have been established, atstep 1305 the symmetry disks are created using a slightly modifiedversion of the clump and shape generation algorithm discussed above.Once these shapes have been created at step 1305, at step 1306 thesymmetry test is performed to validate the weaker Low Level Wind ShearAlert System microburst shapes. The LLWAS microburst shapes and symmetrydisks are the input to this step. Any Low Level Wind Shear Alert Systemmicroburst shape whose magnitude is equal to or above a threshold valueautomatically passes the test. Otherwise, a circumscribing disk iscreated around each of these weak shapes and a test is performed to seewhether a given Low Level Wind Shear Alert System disk is close to anysymmetry disk. If it is, then that Low Level Wind Shear Alert Systemshape passes the test. The output of this process is a list of logicalvalues for each of the input Low Level Wind Shear Alert Systemmicroburst shapes to indicate results of this symmetry test with a truevalue indicating that the shape has passed the test and is a valid foruse in creating a microburst alert.

Microburst Integration

The microburst integration apparatus 114 is the driver of the microburstportion of the integration apparatus. This apparatus converts theTerminal Doppler Weather Radar microburst shapes and validatedmicroburst shapes output by symmetry test apparatus 113 and the LowLevel Wind Shear Alert System microburst shapes into runway specificalerts for any regions on the operational runways (arrival R1, departureR1, etc.) that are defined for the physical runways R1-R4 in theassociated predetermined area which are affected by the shapes. Theregions so affected are combined with the Low Level Wind Shear AlertSystem runway oriented loss alarms. The Low Level Wind Shear AlertSystem inputs to this microburst integration apparatus 114 are therunway oriented losses that are the outputs produced by the Low LevelWind Shear Alert System 101. The microburst integration apparatus 114produces arrays containing the magnitude and location of any loss alarmas mapped onto the runway configuration within the predetermined area.The microburst integration apparatus 114 receives Terminal DopplerWeather Radar microburst shapes from the Terminal Doppler Weather Radarsystem 502 and converts these by mapping them into runway specific locusand magnitude indications to produce runway alarms. In addition,microburst shapes that are computed from the Low Level Wind Shear AlertSystem 101 as validated by the symmetry test apparatus 113 are alsoconverted into runway alarms once they have sufficient magnitude or thesymmetry hypothesis of symmetry test apparatus 113 substantiates theirexistence. In addition, any Low Level wind Shear Alert System runwayoriented losses, as produced by Low Level Wind Shear Alert System 101,that are concurrent with any Low Level Wind Shear Alert microburstshapes are converted into alarms and combined with the above notedTerminal Doppler Weather Radar microburst shapes and Low Level WindShear Alert System microburst shapes and output as a combination ofalarms.

(1) Generation of Runway Specific Alerts:

(a) find alerts that would be generated individually by TDWR andvalidated LLWAS microburst shapes. This is done by the inherent TDWRlogic which finds the intersection of a given shape with an "alert box"(nominally a rectangle around the operational runway path--nautical mileto either side and extending to 3 N.Mi off the runway end). This is donefor each microburst shape. [The LLWAS-generated runway-oriented-loss(ROL) value(s) are only used when an LLWAS microburst shape isgenerated--but then not validated via the symmetry-test algorithm.]Thenthe overall alert for the given operational runway is computed byfinding the "worst-case" magnitude and "first-encounter" location: fromall the "interesting" shapes and the ROL's for the runway.

(2) Display Information:

(a) The above logic is for generating the runway alerts. Thatinformation is then relayed to the ribbon display terminals for the airtraffic controllers, who then transmit it to any impacted aircraft. Thesame information is also displayed on the geographical situation displayby "lighting-up" the appropriate runway locations.

(b) The TDWR and validated LLWAS microburst shapes are also displayed onthe geographic display terminals.

The above-mentioned "worst-case" magnitude and "first-encounter" logicis further applied down-stream after the gust-front integration alertsare separately generated. That is, there can--and often is multipletypes of alerts for a given operational runway. Again, to avoiduser-interpretation and confusion issues, only one alert is generatedfor a given operational runway at a given time. Therefore, the abovelogic is applied for all alerts for a runway. That is, alerts areseparately generated for losses microbursts etc. and gains (gust fronts,etc.) then a single "worst-case" alert is generated. However, microburstalerts (losses 30 knots) always take precedence. That is, if there isconcurrently a 35 knot loss and a 45 knot gain--the 35 knot loss isused. This is because a wind shear that would generate a very hazardousloss (i.e. ≧30 knots) is considered to be more significant for theaircraft.

Additional Data Acquisition Subsystems

The above description of the improved low-level wind shear alert system100 is simply exemplary of the type of aviation weather apparatus thatare available for use in implementing the virtual reality imaging system10 in an aviation weather application. Additional data acquisitionapparatus can include lightning detectors, gust front tracking systems,weather radar to identify the presence and locus of storm cells andprecipitation, icing condition detection systems, aircraft trackingradar, etc. Each of these systems produce data indicative of thepresence, locus, nature and severity of various meteorological phenomenaof interest to aviation operations. In addition, topological data suchas a LANDSAT image of the land surface within the predeterminedmultidimensional space is also available. Other spatial features of themultidimensional space, such as aircraft operations, restrictedairspace, airport locations, etc. are also data inputs that areavailable in the form of static or dynamic data from existinginstrumentation or input to graphics subsystem 2 as initialization data.In summary, there are numerous sources of the data relevant to theuser's needs and the graphics subsystem 2 integrates these data sourcesand filters the received data to create a simplified image of themultidimensional space for the user to enable the user to perform adesired task without being overwhelmed by the quantity of data orwithout having to ignore major sources of data due to the user'sinability to absorb and process the quantity of data that is available.

Graphics Subsystem Architecture

FIG. 1 also illustrates additional detail of an implementation of thegraphics subsystem 2. The virtual reality imaging system 10 can serve aplurality of users, with each user defining a particular image set thatis to be displayed. Therefore, the graphics subsystem 2 can be equippedwith a number of graphics processing apparatus 31-3m. Each of graphicsprocessing apparatus 31-3m receives data input from one or more dataacquisition apparatus 21-2n in the form of raw data or compact datarepresentations. The graphics processing apparatus 31-3m convert thereceived data into images as described below.

Within a graphics processing apparatus 31, graphical object generatormodule 4 functions to convert the raw data or compact datarepresentations received from an associated data acquisition apparatus 1into graphical objects that are later manipulated to produce therequired images. Each graphical object generator module 4 includes aplurality of graphical object generators 41-4k that are described inadditional detail below. The graphical objects that are produced by agraphical object generator module 4 are stored in database 3, along withviewing data input by user interface 5. The user input interface can bea simply terminal device, such as a keyboard, to define a single userselected view, or can be a device to input a continuous stream of dataindicative of a continuously changing user defined view. This latterdevice can be a set of sensors worn by the user that sense the user'shead position to thereby enable the virtual reality imaging system 10 topresent the instantaneous virtual field of view to the user that ispresently in the user's field of vision.

The database 3 is also connected to presentation subsystem 6 thatconverts the data stored in database 3 into a visual image for the user.Presentation subsystem 6 includes element 301 which functions toinitialize the visual display that is to be produced for the one or moreusers. This function clears the processing elements that comprise thegraphics processing apparatus 31 and determine which of the sets of datasets contained in database 3 are to be used to produce the graphicimages for the selected user. Element 302 determines whichcharacteristics or parameters of the data contained in the database 3are to be displayed to this particular designated user. The determinedparameters are then transported along with the raw data obtained fromdatabase 3 to the render graphical objects element 303 which performsthe data merging, transposition and whatever other processing steps arerequired to produce the visual image. The image that is produced by therender graphical objects element 303 is then transmitted by theappropriate transmission media to the display 11 that corresponds to theparticular user.

Image Presentation

Examples of the views that are created by this apparatus are illustratedin FIGS. 13--17. These views are in the context of an airport weathersystem, wherein the displays illustrate an overview of the weather in apredetermined space, as viewed from above in FIG. 13 and in successiveviews of FIGS. 14-17 that are presented to a pilot or an air trafficcontroller illustrating the flight path taken by an aircraft to approachand line up with a particular selected runway 79 at the airport. As canbe seen from these views, there are a plurality of weather phenomena inthe multidimensional space. The weather phenomena include wind shearevents 91--98, thunderstorms P and gust fronts G. The displayillustrates not the phenomena per se but filtered versions thereof thatindicate to the pilot of the aircraft only the significant featuresthereof in order to enable the pilot to avoid any sections of thisphenomena that are most dangerous to the operation of the aircraft. Inparticular, the thunderstorms P may include wind shear events 91-98 thatare extremely dangerous for aircraft operations. The view from thecockpit of the weather phenomena may be totally obscured due to rain,fog or clouds or snow and the illustrations provided in FIGS. 13--17 areindicative of how these visually obscuring events can be eliminated bythe apparatus of the virtual reality imaging system 10 to provide thepilot with a clear indication of the existence of hazards in the path ofthe aircraft or adjacent thereto. The pilot can therefore avoid thesehazards using the virtual reality presented by the apparatus of thepresent invention. By flying along the clear flight path as indicated bythe display, the pilot can avoid all weather phenomena that are obscuredby the visually occluding phenomena without having to be instructed bythe air traffic controllers. Furthermore, the air traffic controllerscan make use of the capability of this system to visually determineproposed flight path through the weather to identify preferred routesfor aircraft operations. This capability can be initiated via userinterface 5, wherein an air traffic controller moves a cursor on thescreen of a display, such as 11, to select one of the plurality ofaircraft A in the multidimensional space. This aircraft selection istranslated, using aircraft position, altitude and heading data receivedfrom an aircraft tracking radar data acquisition subsystem and stored indatabase 3, into a set of coordinates indicative of a point in themultidimensional space. A predefined field of view for an aircraft of aparticular type is also retrieved from the database 3 and used to createthe graphic image for the user.

An example of a visually obscuring event is precipitation. The range ofintensity of precipitation can be divided into a plurality ofcategories, for example on a range of 0 (clear) to 6 (nasty). A level 1precipitation is characterized by clouds and/or light rain that causessome minimum impact on visibility, such that aircraft flying throughlevel 1 precipitation usually will rely on instruments for guidancerather that exclusively on visual guidance. Level 3 precipitation ischaracterized by clouds and moderate rain with a more significant impacton visibility. Lightning is possible in level 3 precipitation and oftenemanates form the higher level precipitation regions that are typicallyembedded in a level 3 region and can strike outside the higher levelregion. Aircraft can usually fly through level 3 precipitation but it istypically avoided whenever possible due to the air turbulenceencountered therein. A level 5 precipitation region is characterized byclouds, heavy rain, and/or hail with lightning and heavy turbulenceoften present. A level 5 region of precipitation represents a region toavoid due to the hazards encountered in flying through this region.

In visually representing these various regions of precipitation, thelevel 1 iso-surface represents the rough "extent of the weather", whilethe higher level regions represent "weather impacted airspace" that liewithin the level 1 region. The iso-surfaces that are displayed ondisplay 11 can be opaque or semi-transparent. If opaque, only the lowestlevel precipitation iso-surface is displayed since the other higherlevel regions are nested inside of this iso-surface and cannot be seen.If a semi-transparent display is selected, then the nested regions ofhigher precipitation can be seen through the semi-transparent exterioriso-surface as darker iso-surfaces or regions displayed by aniso-surface of a contrasting color.

Rendering Process and Apparatus

The preferred embodiment of the virtual reality imaging system of thepresent invention makes use of weather phenomena as one of the objectsdisplayed to the user. In order to better understand the operation ofthe graphics subsystem 2, the rendering of a microburst shape isdescribed in additional detail. The concept of rendering is related toapparatus that creates a synthetic image of an object. The rendererapparatus creates a shaded synthetic image of the object based uponthree-dimensional geometric descriptions, a definition of surfaceattributes of the object and a model of the illumination present in thespace in which the object resides. The final image produced by therenderer apparatus is spatially correct in that surfaces are orderedcorrectly from the observer, and the surfaces appear illuminated withinthe scope of the illumination model. Surfaces may be displayed in amanner to enhance the texture of the surface to highlight that feature,or reflection images on the object can be displayed. Renderer 303 can bea separate processing element or can be software running on a processorshared by other elements displayed in FIG. 1. If an object is to berendered, a description of the object is passed from database 3 to therenderer 303, where the object definition is merged with other soretrieved object definitions to create the image.

This apparatus functions in a manner analogous to the operation of acamera. The camera's position, aiming direction and type of lens mustall be specified to produce the visual image. This information iscreated by determine viewing parameters element 302 from the data storedin database 3 by the user via user interface 5. The viewing parametersare used by the renderer 303 to determine the field of view of thecamera and to delete objects or sections of objects that are obscured bythe presence of other objects that are located closer to the camera andin the line of sight of the camera. As the camera moves along a path,the camera views the objects in a different perspective. The renderer303 creates an image for each predefined interval of time and/or spaceas the camera traverses the path.

The representation of each object in the predetermined multidimensionalspace is accomplished by defining the object as an interconnection of aplurality of polygons and lines. For a polygon that comprises atriangle, its definition is accomplished by specifying the location inthree-dimensional space of the three vertices of the triangle. Therenderer 303 uses the data defining the three vertices to determinewhether the two-dimensional space encompassed by the three sides of thetriangle that interconnect the three vertices is within the field ofview of the camera. If so, the renderer must also determine whether thetriangle is partially or fully obscured by other triangles alreadyretrieved from the database, which triangles define the surfaces ofother objects. Additional complexity is added to this task by theinclusion of color, texture, opacity as defining terms to the object.When the renderer 303 traverses all objects defined in the database andincluded within the field of vision, the graphical image is completedand the resultant image is transferred to the display 11.

A microburst graphical image comprises a collection of the primitivegraphical objects, such as triangles and lines. If triangles are used todefine the surfaces of a microburst, a plurality of triangles areassembled together to create a multi-faceted series of surfaces toproject the image of a solid object. Data defining the vertices of eachtriangle and other relevant surface features, are stored in thegraphical object segment of the database 3. When the renderer 303traverses the database 3, a user interface virtual reality definitiondatabase is queried to identify the filter parameters that are used todefine the objects and features that are of interest to the specificuser. These filter parameters can be microburst magnitude, proximity toan airport runway or aircraft flight path, direction of movement, etc.The additional objects are also retrieved from the database 3, such asother aircraft, precipitation, gust fronts, terrain features, etc. Eachobject is defined in terms of the polygonal shapes and their locationand extent within the predefined volume. Additional primitives can beincluded in this system, such as object transparency, native color,graphical representation color, etc.

Compact Data Representation Conversion To Object Model

In order to illustrate the use of polygonal shapes to define objectsurfaces, the microburst shapes described above are illustrated inthree-dimensional form. The compact data representation of themicroburst shown in FIG. 8 is a band-aid shaped two-dimensional regionon a surface that is used to indicate the location and extent of anaviation hazard. This shape can be defined in terms of two points and aradius as noted by their Cartesian coordinates--(X_(e1), Y_(e1)) andX_(e2), Y_(e2)), and a radius R. The magnitude of the wind shear in thismicroburst is additional data that defines this wind shear event and maynot be used initially to produce a representation of the wind shearevent, since as described above, all wind shear events in excess of apredetermined magnitude are considered to be a hazard and should bedisplayed. However, the user's filter definition may include microburstseverity parameters that modulate the standard wind shear eventthreshold data to display more or fewer microbursts, as a function ofthe user's filter. The Cartesian Coordinate system is defined inrelation to the multidimensional space.

A microburst is a surface related phenomena, in that the aircraft hazardonly exists when the aircraft is in close proximity to the ground, suchas in a final approach to an airport runway or immediately upon takeoff.However, a graphical image of the microburst must have three-dimensionalextent to ensure that the user can visually detect its presence in thedisplay 11, especially when viewed from an obtuse angle. The compactdata representation of a microburst as shown in FIG. 7 provides atwo-dimensional definition of the surface locus and extent impacted bythe microburst. The surface is typically not featureless and thistwo-dimensional compact data representation must be translated into athree-dimensional representation of the event. Therefore, the database 3is queried to obtain topological data that defines the surface featurein and around the microburst impact area. The renderer 303 maps thetwo-dimensional locus and extent data into a three-dimensionalrepresentation of the actual surface area impacted by the microburst asillustrated in FIG. 8. In addition, there can be a spreading effect atthe surface and the microburst may be illustrated in a manner that thetop of the microburst is slightly smaller than its extent at thesurface, somewhat like a plateau.

FIGS. 7-12 illustrate details of the method used by this apparatus toconvert the two-dimensional compact data representation of a microburstinto a three-dimensional graphic image representation. In FIG. 7, thetwo-dimensional bandaid shape is represented, including n datum pointsPi and surface normals representative of the two-dimensional tangentvector at each datum point on the curve. Once each datum point isidentified, its location on the three-dimensional surface is definedsuch that Pi=(xi, yi zi). The series of datum points can then beillustrated diagrammatically in FIG. 8 as a perimeter line that followsthe topology of the surface to include the locus and extent defined bythe two-dimensional bandaid shape. The series of datum points so definedin three-dimensional space each have the property that two datacomponents (xi, yi) define the datum point location on the bandaidperimeter in two-dimensional space. A third component defines the heightof the datum point above a base level in the multidimensional space. Thenormal vectors generated for each datum point define the "outward"direction of the surface of the microburst at that datum point. Thenormal vectors can be used for shading and to provide the appearance ofa smooth surface.

Once the series Pi of datum points are defined as described above, asecond series of datum points Qi are defined in three-dimensional spaceto define the top perimeter of the microburst. FIG. 9 illustrates therelationship of the first and second series of datum points intwo-dimensional space. The second series of datum points are selected sothat their perimeter defines a bandaid shape of locus and extent lessthan that of the perimeter defined by the first series of datum pointsso that the second series are inscribed within the first series of datumpoints. The third component of each datum point in the second series ofdatum points is selected to be a fixed height above the surface locationof a corresponding datum point in the first series of datum points. Theresultant three-dimensional shape is illustrated in FIG. 10 andresembles a plateau whose top surface follows the contours of thesurface on which it rests.

To enable the renderer to perform its task, the microburst shape definedby the two series of datum points are converted to a plurality ofsurface defining polygons. FIG. 11 illustrates the definition of oneside surface of the microburst while FIG. 12 illustrates the definitionof the top of the microburst. In particular, in FIG. 11, a triangularsegment of surface 1 results from connecting the two series of datumpoints with lines that define the edges of triangle. A first triangle isdefined by connecting points (P1, Q1, Q2) while a second triangle isdefines by connecting points (P1, P2, Q2). This process is continueduntil the last defined triangle connects to the original or startingpoints P1, Q1. A similar process is used to define the top of themicroburst shape.

The exterior surfaces of the microburst are thus defined by a series oftriangles, each of which is precisely located within themultidimensional space by the Cartesian Coordinates of the vertices ofeach triangle. The renderer can then use the vertices and the surfacenormal vectors to denote surfaces in the field of vision of the user andto represent the obscuration of one object by another. Once the field ofvision is thus defined, the additional attributes of the various objectsare used in conjunction with the user-defined filters to transform thevisual image into the virtual reality defined by the user. Inparticular, the types of objects displayed can be defined by the user toeliminate the visually obscuring effects of precipitation, or fog, orclouds. In addition, the surface defined by the triangles and thesurface normal vectors can be visually displayed using shading as afunction of the magnitude and direction of each surface defining vectorto provide the user with an accurate three-dimensional solidrepresentation of the object. Furthermore, time sequential values forthe location of each datum point can be used to provide a "moving image"representation of the object to illustrate its movement in themultidimensional space over a number of time-sequential time intervals.This time series of data points can also be extrapolated to predict thefuture movement of the microburst.

Display Example

FIGS. 13-17 illustrate an example of a series of displays that can beused in an aircraft weather display application of the virtual realityimaging system 10. FIG. 13 illustrates a top view of a predeterminedspace, which view includes a plurality of features and phenomena. Thepredetermined space is delineated by boundary lines B and range circlesR1-R3 can be included to denote range form a predetermined point locatedin the predetermined space. Temporally constant features located in thisspace are roads H1-H6 and natural topological features, such asmountains M. Other features shown on FIG. 13 are airport runway 79 andaircraft A. Temporally and spatially varying phenomena that are presentin this space are regions of precipitation P, wind shear events 91-98,and gust front G. Collectively, these elements represent the items ofinterest to aircraft operations in the predetermined space. It isobvious that a level of precipitation is selected for the operation ofdisplay 11 to delineate in FIG. 13 only the extent of the selected levelof precipitation to minimize the complexity of the display shown inFigure 13. This feature enables the viewer to minimize the extent of theprecipitation regions displayed to only the levels of interest. Forexample, if only level 3 or higher regions of precipitation aredisplayed, then the visual obscuration presented by the level 1 and 2precipitation are deleted from the display thereby enhancing thevisibility for the viewer, such as a pilot.

A more useful display for a pilot is shown in FIG. 14 where the used viauser interface 5 defines a point in the predetermined space and a fieldof view. This data is translated into a perspective three-dimensionaltype of display to illustrate the view from the selected point, with theobscuring data filtered out by the renderer 303. As noted above, the lowlevel precipitation and other aircraft A can be removed from the viewand only objects of interest displayed thereon. The display therebypresents an image of the potentially threatening weather phenomena thatconfronts the pilot, in the form of precipitation P of at least level 3,wind shear events 91-98 and surface topology S. Note that the boundariesB of the predetermined space are also shown to indicate the extent ofthe data present in the field of view. As the aircraft decreases itsaltitude and approaches the runway 79, the field of view and point inthe predetermined space change as the aircraft traverses the flightpath. The virtual reality imaging system 10 periodically samples thedata to update the point in space, field of view as well as selectedcharacteristics for display to create ever changing images, such as thatshown in FIG. 15, as the aircraft traverses the predetermined space tocircle the runway 79 an line up for an approach. The view on approach isshown in FIG. 16 where the runway 79 is clearly seen as are regions ofprecipitation P and wind shear events 96-98. These wind shear events9698 in reality may not be visible to the pilot and the display via acomputer generated rendering provides the pilot with information ofweather related phenomena that otherwise is unavailable. The pilot candetermine the proximity of the wind shear events 96-98 to runway indeciding whether to continue the approach to runway 79. In addition,renderer 303 can be activated to extrapolate the existing data toillustrate a likely progression of wind shear events 96-98 that islikely to occur during the aircraft approach. This predicted scenariocan be quickly displayed on display 11 to enable the pilot to determinethe closest approach of wind shear events 96-98 during the entirelanding operation. FIG. 17 illustrates a timewise sequential displayfollowing that of FIG. 16 to indicate the display that would be seenfurther along the flight path as the runway 79 is approached.

SUMMARY

As can be seen from the above examples, the virtual reality imagingsystem displays features and phenomena, that can be temporally and/orspatially varying, in a manner to filter out the characteristics of thefeatures and phenomena that are not of interest to the viewer. The imagepresented to the viewer is a condensation of all the data collected bythe plurality of data acquisition systems, and some of the datapresented represents features or phenomena that are not visible to theviewer with the naked eye. Thus, this apparatus operates in real time toprovide each user with a customized view of a predetermined space toenable the user to perform a desired task.

While a specific embodiment of this invention has been disclosed, it isexpected that those skilled in the art can and will design alternateembodiments of this invention that fall within the scope of the appendedclaims.

I claim:
 1. Apparatus for presenting a user with a virtual image in realtime of phenomena located in a predefined multidimensional space,comprising:means for generating data, in real time, indicative of atleast one phenomena extant in a multidimensional space and traversingsaid multidimensional space which multidimensional space has predefinedextent in a plurality of dimensions, comprising: means for measuringpresent values of a plurality of parameters for at least one temporallyvariant phenomena extant in said multidimensional space; means forstoring data defining a plurality of characteristics of said phenomenathat are to be displayed to a user; means for filtering said generateddata to extract data that satisfies said plurality of characteristics,defined by said stored data, from said generated data, comprising:meansfor storing data indicative of a predefined point in saidmultidimensional space; means for storing data indicative of a field ofview from said predefined pointing said multidimensional space: means,responsive to said stored predefined point and field of view data fordefining a segment of said multidimensional space representative of saidfield of view as taken from said predefined point; means for identifyinga portion of any of said phenomena that lie within said segment of saidmultidimensional space; and means, responsive to said extracted data,for producing an image representative of a three dimensional view of atleast a portion of said multidimensional space to display saidphenomena, substantially temporally concurrent with the generation ofsaid data indicative of said at least one phenomena extant in amultidimensional space and used to produce said image.
 2. The apparatusof claim 1 wherein said generating means comprises:means for displayingsaid plurality of characteristics of said portion of said phenomenaincluded in said segment of said multidimensional space.
 3. Theapparatus of claim 2 wherein said extracting means furthercomprises:means for defining a motion vector through saidmultidimensional space from said predefined point to a terminationpoint; means for defining a sampling interval; means, responsive to saidstored data indicative of said predefined point, said motion vector,said sampling interval and said field of view, for defining a pluralityof segments of said multidimensional space representative of said fieldof view as taken from said predefined point at each of a plurality ofsuccessive sample intervals, as said motion vector traverses saidmultidimensional space from said predefined point to said terminationpoint.
 4. The apparatus of claim 3 wherein said generating means furthercomprises:means for extrapolating a present one of said measured valuesof said plurality of parameters for at least one sampling interval intothe future from a present sampling interval.
 5. The apparatus of claim 4wherein said means further comprises:means for defining a samplinginterval; means for extrapolating a present one of said measured valuesof said plurality of parameters for at least one sampling interval intothe future from a present sampling interval; means, responsive to saidstored data indicative of said predefined point, said sampling interval,said measured present values of said plurality of parameters, saidextrapolated parameters and said field of view, for displaying saidfield of view as taken from said predefined point at each of a pluralityof successive sample intervals including said present sampling intervaland said at least one sampling interval into the future.
 6. Theapparatus of claim 1 wherein said generating means comprises:means formeasuring a plurality of parameters for at least one temporallyinvariant phenomena extant in said multidimensional space.
 7. Theapparatus of claim 1 wherein said generating means comprises:means forsimulating a phenomena in said multidimensional space.
 8. A method forpresenting a user with a virtual image in real time of phenomena locatedin a predefined multidimensional space, comprising the stepsof:generating data, in real time, indicative of at least one phenomenaextant in a multidimensional space, which multidimensional space haspredefined extent in a plurality of dimensions, comprising measuringpresent values of plurality of parameters for at least one temporallyvariant phenomena extant in multidimensional space; storing datadefining a plurality of characteristics of said phenomena that are to bedisplayed to a user; extracting data that satisfies said plurality ofcharacteristics, defined by said stored data, from said generated data,comprising: storing data indicative of a predefined point in saidmultidimensional space; storing data indicative of a field of view fromsaid predefined point in said multidimensional space; defining inresponse to said stored predefined point and field of view data, asegment of said multidimensional space representative of said field ofview as taken from said predefined point; identifying a portion of anyof said phenomena that lie within said segment of said multidimensionalspace; and producing, in response to said extracted data, an imagerepresentative of a three dimensional view of at least a portion of saidmultidimensional space to display said phenomena, substantiallytemporally concurrent with the generation of said data indicative ofsaid at least one phenomena extant In a multidimensional space and usedto produce said image.
 9. The method of claim 8 wherein said step ofgenerating comprises:displaying said plurality of characteristics ofsaid portion of said phenomena included in said segment of saidmultidimensional space.
 10. The method of claim 8 wherein said step ofextracting further comprises:defining a motion vector through saidmultidimensional space from said predefined point to a terminationpoint; defining a sampling interval; defining, in response to saidstored data indicative of said predefined point, said motion vector,said sampling interval and said field of view, a plurality of segmentsof said multidimensional space representative of said field of view astaken from said predefined point at each of a plurality of successivesample intervals, as said motion vector traverses said multidimensionalspace from said predefined point to said termination point.
 11. Themethod of claim 10 wherein said step of generating furthercomprises:extrapolating said measured present values of said pluralityof parameters for at least one sampling interval into the future from apresent sampling interval.
 12. The method of claim 9 wherein said stepof extracting further comprises:defining a sampling interval;extrapolating said measured present values of said plurality ofparameters for at least one sampling interval into the future from apresent sampling interval; displaying, in response to said stored dataindicative of said predefined point, said sampling interval, saidmeasured present values of said plurality of parameters, saidextrapolated parameters and said field of view, said field of view astaken from said predefined point at each of a plurality of successivesample intervals including said present sampling interval and said atleast one sampling interval into the future.
 13. The method of claim 8wherein said step of generating comprises:measuring a plurality ofparameters for at least one temporally invariant phenomena extant insaid multidimensional space.
 14. The method of claim 8 wherein said stepof generating comprises:simulating a phenomena in said multidimensionalspace.
 15. Apparatus for presenting a user with a virtual image in realtime of meteorological phenomena located in a predefinedmultidimensional space, comprising:means for generating data indicativeof at least one meteorological phenomena extant in e multidimensionalspace, which multidimensional space has predefined extent in a pluralityof dimensions, comprising:means for measuring a plurality of parametersfor at least one temporally variant meteorological phenomena extant insaid multidimensional space, means for measuring a plurality ofparameters for at least one spatial feature extant in saidmultidimensional space; means for storing data defining a plurality ofcharacteristics of said meteorological phenomena and spatial featuresthat am to be displayed to a user; means for storing data indicative ofa predefined point in said multidimensional space; means for storingdata indicative of a field of view from said predefined point in saidmultidimensional space; means, responsive to said stored predefinedpoint and field of view date, for defining a segment of saidmultidimensional space representative of said field of view as takenfrom said predefined point; means for identifying a portion of any ofsaid meteorological phenomena and spatial features that lie within saidsegment of said multidimensional space; means for extracting data thatsatisfies said plurality of characteristics, defined by said stored dam,from said generated data for any said identified portion of any of saidmeteorological phenomena and spatial features that lie within saidsegment of said multidimensional space; and means for displaying saidplurality of characteristics of said meteorological phenomena and saidspatial features inclusive in said segment of said multidimensionalspace, substantially temporally concurrent with the generation of saiddata indicative of said meteorological phenomena extant in amultidimensional space and used to produce said image.
 16. The apparatusof claim 15 wherein said multidimensional space comprises a volume ofairspace, said meteorological phenomena measuring means comprises:atleast one weather data acquisition system for producing data indicativeof weather phenomena in said multidimensional space.
 17. The apparatusof claim 16 wherein said multidimensional space comprises an airspace,said spatial feature measuring means comprises;topological dataacquisition system for producing data indicative of surface topologicalfeatures in said multidimensional space.
 18. The apparatus of claim 17wherein said characteristics storing means comprises:means, responsiveto user input data, for defining a set of thresholds indicative ofweather phenomena magnitude.
 19. The apparatus of claim 18 wherein saidpredefined point storing means stores data indicative of a position ofan aircraft, said field of view storing means stores data indicative ofa field of view in the flight path of said aircraft, said displayingmeans comprises:means for producing a visual display for a pilot of saidaircraft of said weather phenomena exceeding said threshold that areinclusive of said field of view in said flight path of said aircraft.20. The apparatus of claim 19 wherein said extracting means furthercomprises:means for defining a motion vector through saidmultidimensional space from said predefined point to a terminationpoint; means for defining a sampling interval; means, responsive to saidstored predefined point, motion vector, sampling interval and field ofview data, for defining a plurality of segments of said multidimensionalspace representative of said field of view as taken from said predefinedpoint at each of a plurality of successive sample intervals, as saidmotion vector traverses said multidimensional space from said predefinedpoint to said termination point.
 21. The apparatus of claim 20 whereinsaid displaying means comprises:means for displaying in each saidsuccessive sample intervals, said plurality of characteristics of saidmeteorological phenomena and said multidimensional space inclusive ofeach said segment of said multidimensional space.
 22. The apparatus ofclaim 21 wherein said generating means comprises:means for extrapolatinga present one of said measured values of said plurality of parametersfor at least one interval of time into the future; and means fordisplaying said plurality of extrapolated characteristics of saidmeteorological phenomena and said multidimensional space.
 23. Theapparatus of claim 20 wherein said extracting means furthercomprises:means for defining a sampling interval; means forextrapolating a present one of said measured values of said plurality ofparameters for at least one sampling interval into the future from apresent sampling interval; means, responsive to said stored dataindicative of said predefined point, said sampling interval, saidmeasured present values of said plurality of parameters, saidextrapolated parameters and said field of view, for displaying saidfield of view as taken from said predefined point at each of pluralityof successive sample intervals including said present sampling intervaland said at least one sampling interval into the future.
 24. A methodfor presenting a user with a virtual image in real time ofmeteorological phenomena located in a predefined multidimensional space,comprising the steps of:generating data indicative of at least onemeteorological phenomena extant in a multidimensional space, whichmultidimensional space has predefined extent in a plurality ofdimensions, comprising:measuring a plurality of parameters for at leastone temporally variant meteorological phenomena extant in saidmultidimensional space, measuring a plurality of parameters for at leastone spatial feature extant in said multidimensional space; storing datadefining a plurality of characteristics of said meteorological phenomenaand spatial features that are to be displayed to a user; storing dataindicative of a predefined point in said multidimensional space; storingdata indicative of a field of view from said predefined point in saidmultidimensional space; defining, in response to said stored predefinedpoint and field of view data, a segment of said multidimensional spacerepresentative of said field of view as taken from said predefinedpoint; and identifying a portion of any of said meteorological phenomenaand spatial features that lie within said segment of saidmultidimensional space; extracting data that satisfies said plurality ofcharacteristics, defined by said stored data, from said generated datafor any said identified portion of any of said meteorological phenomenaand spatial features that lie within said segment of saidmultidimensional space; displaying said plurality of characteristics ofsaid meteorological phenomena and said spatial features inclusive insaid segment of said multidirectional space, substantially temporallyconcurrent with the generation of said data indicative of saidmeteorological phenomena extant in a multidimensional space end used toproduce said image.
 25. The method of claim 24 wherein saidmultidimensional space comprises a volume of airspace, said step ofmeteorological phenomena measuring comprises producing data indicativeof weather phenomena in said multidimensional space using at least oneweather data acquisition system.
 26. The method of claim 25 wherein saidmultidimensional space comprises an airspace, said step of spatialfeature I measuring comprises producing data indicative of surfacetopological features in said multidimensional space using a topologicaldata acquisition system.
 27. The method of claim 26 wherein said step ofcharacteristics storing comprises:defining, in response to user inputdata, a set of thresholds indicative of weather phenomena magnitude. 28.The method of claim 27 wherein said step of predefined point storingstores data indicative of a position of an aircraft, said field of viewstoring means stores data indicative of a field of view in the flightpath of said aircraft, and said step of displaying comprises:producing avisual display for a pilot of said aircraft of said weather phenomenaexceeding said threshold that are inclusive of said field of view insaid flight path of said aircraft.
 29. The method of claim 28 whereinsaid step of extracting further comprises:defining a motion vectorthrough said multidimensional space from said predefined point to atermination point; defining a sampling interval; defining, in responseto said stored predefined point, motion vector, sampling interval andfield of view data, a plurality of segments of said multidimensionalspace representative of said field of view as taken from said predefinedpoint at each of a plurality of successive sample intervals, as saidmotion vector traverses said multidimensional space from said predefinedpoint to said termination point.
 30. The method of claim 27 wherein saidstep of displaying comprises:displaying in each said successive sampleintervals, said plurality of characteristics of said meteorologicalphenomena and said multidimensional space inclusive of each said segmentof said multidimensional space.
 31. The method of claim 30 wherein saidstep of generating comprises:extrapolating a present one of saidmeasured values of said plurality of parameters for at least oneinterval of time into the future; and displaying said plurality ofextrapolated characteristics of said meteorological phenomena and saidmultidimensional space.
 32. The method of claim 31 wherein said step ofextracting further comprises:defining a sampling interval; extrapolatinga present one of said measured values of said plurality of parametersfor at least one sampling interval into the future from a presentsampling interval; displaying, in response to said stored dataindicative of said predefined point, said sampling interval, saidmeasured present values of said plurality of parameters, saidextrapolated parameters and said field of view, said field of view astaken from said predefined point at each of a plurality of successivesample intervals including said present sampling interval and said atleast one sampling interval into the future.